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Record W3043500426 · doi:10.1139/facets-2019-0014

Conserving plant species at risk in Canada: land tenure, threats, and representation in federal programs

2020· article· en· W3043500426 on OpenAlexaffvenueabout
Jenny L. McCune, Peter D.S. Morrison

Bibliographic record

VenueFACETS · 2020
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsCarleton University
Fundersnot available
KeywordsStewardship (theology)HabitatPlant speciesGeographyLand tenureEcologyBusinessEnvironmental resource managementAgroforestryBiologyPolitical scienceAgricultureEconomicsPolitics

Abstract

fetched live from OpenAlex

Fully 37% of species listed under Canada’s Species at Risk Act (SARA) are plants or lichens. The law does not automatically protect species on private land, and it is unknown how many at-risk plants grow mainly on private land. We analyzed official status reports and related documents for 234 plant species at risk to determine land tenure and evaluated differences in threats and changes in status. We also assessed how well plants were represented in two federal programs: the Natural Areas Conservation Program (NACP) and the Habitat Stewardship Program (HSP). Of SARA-listed plant species, 35% have the majority of their known populations on private land while <10% occur mostly on federal land. Species growing mainly on private land were no more or less likely to decline in status over time compared with others. Plant species at risk were less likely than other taxonomic groups to be found on land protected under the NACP. The proportion of HSP projects targeting plants is well below the expected proportion based on the number of listed species. We recommend that policy-makers promote and prioritize actions to increase the representation of plant species in federally funded programs, especially on private lands.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.079
GPT teacher head0.295
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2020
Admission routes3
Has abstractyes

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